Abstract Models of intercellular communication in tissues are based on molecular profiles of
dissociated cells, are limited to receptor–ligand signaling and ignore spatial proximity in situ …

It is poorly understood how different cells in a tissue organize themselves to support tissue
functions. We describe the CytoCommunity algorithm for the identification of tissue cellular …

Inference of cell–cell communication from single-cell RNA sequencing data is a powerful
technique to uncover intercellular communication pathways, yet existing methods perform …

Spatial transcriptomic technologies and spatially annotated single-cell RNA sequencing
datasets provide unprecedented opportunities to dissect cell–cell communication (CCC) …

The rapid emergence of spatial transcriptomics (ST) technologies is revolutionizing our
understanding of tissue spatial architecture and biology. Although current ST methods …

Motivation Recent years have seen the release of several toolsets that reveal cell–cell
interactions from single-cell data. However, all existing approaches leverage mean celltype …

The flood of genome-wide data generated by high-throughput technologies currently
provides biologists with an unprecedented opportunity: to manipulate, query, and …

No cell lives in a vacuum, and the molecular interactions between cells define most
phenotypes. Transcriptomics provides rich information to infer cell–cell interactions and …

Computational methods that model how gene expression of a cell is influenced by
interacting cells are lacking. We present NicheNet (https://github. com/saeyslab/nichenetr), a …

Single-cell RNA-sequencing data has revolutionized our ability to understand of the patterns
of cell–cell and ligand–receptor connectivity that influence the function of tissues and …